New Book: AI Product Manager

How to Build, Evaluate, and Evolve Products in the Age of AI Agents

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Brief Synopsis

AI Product Manager: How to Build, Evaluate, and Evolve Products in the Age of AI Agents is a practical guide for Product Managers, founders, designers, and technology leaders building products in the new era of AI agents.

The book shows how to use AI not simply as a tool or an additional product feature, but as part of the entire product development system—from user research and hypothesis validation to prototyping, development, evaluation, launch, and continuous improvement.

Readers will learn how to design AI-native products, decide when to use a conventional workflow versus an AI agent, work with models, context, RAG, tools, and MCP, build evaluation systems, control costs and risks, and turn user feedback into a continuous product improvement loop.

What Readers Will Gain

After reading the book, readers will be able to:

• Turn ideas into working prototypes faster without immediately requiring a large engineering team.
• Identify where AI creates genuine value and where it only adds unnecessary complexity.
• Design AI-native products and agents with the appropriate level of autonomy.
• Use AI throughout the Product Management lifecycle—research, discovery, prototyping, development, testing, and feedback.
• Evaluate AI system quality instead of judging products by impressive demos alone.
• Manage the cost, latency, security, and reliability of AI-powered products.
• Build effective workflows between Product Managers, AI tools, and AI agents.
• Make product decisions based on evidence and experimentation, rather than intuition alone.
• Shorten the path from a user problem to a production-ready product.

The Core Value of the Book

The book does not teach a fixed set of AI tools that may quickly become outdated. Instead, it provides a practical framework for building products in the age of AI agents—a methodology that remains applicable regardless of which models, platforms, and technologies emerge next.

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Introduction — The New Product Manager

• What has changed in product development
• From AI features to AI-native products
• The collapse of the distance between idea and prototype
• Why iteration speed has become a product advantage
• What AI can do for a Product Manager—and what it cannot
• The new Product Manager: researcher, builder, operator, and strategist
• The AI Product Development Loop
• How to use this book

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Part I — The Foundations of AI Product Management

Chapter 1 — Product Management After AI

• The traditional product development cycle
• The AI-native product development cycle
• What AI actually accelerates
• The cost of moving in the wrong direction faster
• From roadmaps to continuous decision-making
• From feature delivery to learning velocity
• The new responsibilities of the Product Manager
• What should never be delegated to AI

Chapter 2 — AI-Native Product Thinking

• AI-powered vs. AI-assisted vs. AI-native products
• When AI creates genuine product value
• When AI is the wrong solution
• Designing around capabilities instead of features
• Human-in-the-loop vs. human-on-the-loop
• Copilots, workflows, agents, and autonomous systems
• Designing the minimum necessary level of autonomy
• The AI Opportunity Canvas
• AI feasibility assessment

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Part II — AI-Powered Product Discovery

Chapter 3 — Understanding Users with AI

• Turning unstructured user data into product insight
• Interviews, support tickets, reviews, chats, and behavioral data
• AI-assisted interview analysis
• Identifying patterns without losing context
• Clustering problems and user needs
• From observations to insights
• From insights to hypotheses
• Avoiding AI-generated research bias
• The AI Research Workflow

Chapter 4 — Competitive Intelligence in the AI Era

• What to analyze beyond feature lists
• Product positioning and user workflows
• Monitoring competitors with AI
• Tracking product and pricing changes
• Analyzing customer sentiment
• Identifying gaps and opportunities
• Separating facts from generated conclusions
• Turning competitive intelligence into product decisions

Chapter 5 — From Problem to Product Hypothesis

• Writing a meaningful problem statement
• Jobs-to-be-Done in AI products
• Identifying AI opportunities
• Formulating product hypotheses
• Defining expected outcomes
• Defining success metrics
• Defining kill criteria
• Testing assumptions before building
• The AI Product Hypothesis Canvas

Chapter 6 — Prioritizing AI Opportunities

• Why traditional prioritization models are not enough
• User value
• Frequency and severity of the problem
• AI feasibility
• Model quality
• Cost and latency
• Data availability
• Risk and safety
• Strategic value
• Build, buy, or partner
• API, open-source, or proprietary models
• The AI Opportunity Score

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Part III — From Idea to Working Prototype

Chapter 7 — Building AI Prototypes

• Why static mockups are no longer enough
• Prototype vs. MVP vs. production
• What a prototype should prove
• Designing the smallest useful AI system
• Inputs, processing, model, context, and output
• Simulating AI behavior
• Connecting real data
• Testing the complete user workflow
• From concept to working prototype

Chapter 8 — AI Coding for Product Managers

• How much technical knowledge a modern PM needs
• Understanding frontend, backend, APIs, databases, and deployment
• Working with AI coding agents
• Giving an AI coding agent the right context
• Breaking a product idea into implementation tasks
• Reviewing AI-generated code
• Git, version control, and change management
• Debugging with AI
• Avoiding AI-generated technical debt
• Knowing when to hand the project to engineering

Chapter 9 — Designing AI Workflows

• Prompt vs. workflow vs. agent
• Structured outputs
• Sequential workflows
• Decision points
• Tool calling
• State and memory
• Human approval
• Error handling
• Workflow orchestration
• Choosing the simplest architecture that works
• When not to build an agent

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Part IV — The Architecture of AI Products

Chapter 10 — The Anatomy of an AI System

• Models
• Context
• Knowledge
• Retrieval
• Tools
• Memory
• Orchestration
• Guardrails
• Observability
• Human control
• Designing the complete AI system

Chapter 11 — Context Engineering

• Why prompt engineering is no longer enough
• What belongs in model context
• System instructions
• User input
• Retrieved information
• Conversation history
• Tools and tool results
• State and memory
• Context selection
• Context compression
• Context pollution
• Retrieval-Augmented Generation
• When RAG is the wrong solution
• Managing context cost and latency

Chapter 12 — Tools, APIs, and MCP

• From answering questions to taking actions
• Function calling
• Tool design
• API integration
• Model Context Protocol
• Connecting business systems
• Databases
• Files
• Browsers
• CRM and internal tools
• Read vs. write operations
• Permission boundaries
• Approval workflows
• Auditing agent actions

Chapter 13 — Memory and State

• Why conversation history is not memory
• Working memory
• Long-term memory
• User memory
• Product state
• Agent state
• What should be remembered
• What should never be remembered
• Memory retrieval
• Memory decay and correction
• When memory makes a product worse

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Part V — Building AI Agents

Chapter 14 — From Copilots to Autonomous Agents

• Chat interfaces
• Copilots
• AI workflows
• Agents
• Autonomous agents
• Multi-agent systems
• Increasing autonomy and increasing risk
• Choosing the right level of autonomy
• Where agents create real value
• Where agents create unnecessary complexity

Chapter 15 — Designing Reliable AI Agents

• Defining the agent's goal
• Inputs and context
• Tools
• State
• Memory
• Decision logic
• Permissions
• Success criteria
• Failure conditions
• Stop conditions
• Escalation
• Auditability
• The Agent Specification

Chapter 16 — Agent UX

• Designing for interaction with autonomous systems
• When an agent should ask for permission
• Showing agent state and progress
• Communicating uncertainty
• Explaining actions
• Undo and rollback
• Correcting an agent
• Human takeover
• Failure states
• Designing trust without creating false confidence

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Part VI — Evaluation: Proving That AI Works

Chapter 17 — Why a Successful Demo Proves Nothing

• The difference between demos and reliable products
• Model variability
• Edge cases
• Regression
• Failure at scale
• Hidden failure modes
• Evaluation-driven product development
• Defining quality before launch

Chapter 18 — Building an Evaluation System

• What an evaluation actually measures
• Golden datasets
• Test cases
• Automated evaluation
• Human evaluation
• LLM-as-a-judge
• Pairwise comparison
• Regression testing
• Continuous evaluation
• Evaluating prompts, models, tools, and agents
• Building an evaluation pipeline

Chapter 19 — Measuring AI Product Performance

Product metrics

• Activation
• Retention
• Conversion
• Task completion

AI quality metrics

• Accuracy
• Relevance
• Groundedness
• Hallucination rate
• Tool success rate
• Escalation rate

System metrics

• Latency
• Token usage
• Cost
• Reliability

User metrics

• Acceptance rate

• Correction rate

• Trust

• Abandonment

• Choosing metrics that reflect actual user value

• Building an AI product scorecard

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Part VII — From Prototype to Production

Chapter 20 — Productionizing AI Products

• What changes after the first real users arrive
• Reliability engineering for AI systems
• Monitoring
• Logging
• Observability
• Model versioning
• Prompt versioning
• Tool versioning
• Fallback models
• Human fallback
• Incident management
• Rollbacks
• Continuous improvement

Chapter 21 — AI Security

• Prompt injection
• Data leakage
• Excessive agency
• Tool abuse
• Malicious inputs
• Sensitive information
• Permission boundaries
• Sandboxing
• Audit logs
• Red teaming
• Security by design

Chapter 22 — Privacy and AI Governance

• What data is sent to models
• Data retention
• Access control
• Sensitive data
• Enterprise requirements
• AI usage policies
• Human accountability
• Documentation
• Governance for agentic systems

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Part VIII — The Economics of AI Products

Chapter 23 — AI Unit Economics

• Why AI changes software economics
• Cost per request
• Cost per task
• Cost per user
• Token economics
• Model pricing
• Latency vs. cost
• Caching
• Model routing
• Small vs. large models
• Infrastructure costs
• Gross margin
• Cost-aware product design
• When an AI feature is economically unsustainable

Chapter 24 — Model Strategy

• How to choose a model
• Quality vs. cost vs. latency
• Proprietary APIs vs. open-source models
• Single-model vs. multi-model architectures
• Model routing
• Fine-tuning
• RAG vs. fine-tuning
• Specialized models
• Model substitution
• Vendor lock-in
• Designing for model portability

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Part IX — The AI Product Operating System

Chapter 25 — Building an AI Research System

• Research sources
• Automated collection
• Processing and normalization
• Analysis
• Insight extraction
• Hypothesis generation
• Decision-making
• Research repositories
• Decision logs
• Keeping research continuously updated

Chapter 26 — Building an AI Feedback System

• Collecting product feedback
• Support tickets
• Reviews
• Interviews
• Behavioral signals
• Automatic classification
• Clustering
• Prioritization
• Detecting emerging problems
• Turning feedback into experiments
• Closing the feedback loop

Chapter 27 — Building an AI Meeting System

Before the meeting

• Context gathering
• Agenda
• Questions
• Stakeholder analysis

During the meeting

• Transcription
• Decisions
• Open questions
• Action items

After the meeting

• Owners
• Deadlines
• Documentation
• Follow-up
• Updating product knowledge

Chapter 28 — Building an AI Documentation System

• Product briefs
• PRDs
• Decision logs
• Architecture decision records
• Experiment logs
• Changelogs
• Knowledge bases
• Keeping documentation synchronized with reality
• Documentation as a by-product of product development

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Part X — The AI-Native Product Team

Chapter 29 — The New Product Team

• The evolving role of the Product Manager
• Product Design in AI products
• Software Engineering in the age of AI coding
• AI Engineering
• Data Engineering
• Domain expertise
• Cross-functional AI teams
• Human and AI workers as one operating system

Chapter 30 — The AI-Native Development Process

Stage 1 — Problem

Define the problem.

Stage 2 — Discovery

Understand users and context.

Stage 3 — Hypothesis

Define the opportunity.

Stage 4 — Prototype

Build the smallest useful system.

Stage 5 — Evaluation

Measure quality.

Stage 6 — User Testing

Test with real users.

Stage 7 — MVP

Build the minimum production product.

Stage 8 — Production

Deploy and operate.

Stage 9 — Monitoring

Measure quality, cost, and behavior.

Stage 10 — Iteration

Use evidence to decide what happens next.

For every stage:

• Inputs
• Activities
• AI capabilities
• Outputs
• Decision criteria
• Kill criteria

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Part XI — Real-World AI Product Cases

Chapter 31 — Adding AI to an Existing Product

A complete case study:

Problem → Research → Hypothesis → Prototype → Architecture → Evaluation → Economics → Launch

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Chapter 32 — Building an AI-Native Product from Scratch

A complete greenfield case:

Market opportunity → User problem → AI-native workflow → Agent → Evaluation → MVP → Production

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Chapter 33 — Building an AI Operations Agent

A practical case involving:

• knowledge retrieval;
• classification;
• decision-making;
• tool use;
• automated actions;
• human approval;
• monitoring.

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Chapter 34 — Knowing When to Kill an AI Product

• Poor model quality
• Insufficient user value
• High operating cost
• Low adoption
• Unacceptable risk
• Lack of defensibility
• Better non-AI alternatives

Kill criteria

• How to define them before development
• How to recognize sunk-cost bias
• How to shut down an AI experiment without wasting the learning

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Part XII — The AI Product Manager's Operating System

Chapter 35 — The AI Product Operating System

A unified framework connecting:

Discovery → Decision → Creation → Intelligence → Evaluation → Production → Economics → Learning

• The discovery layer
• The decision layer
• The creation layer
• The intelligence layer
• The evaluation layer
• The production layer
• The economics layer
• The learning layer

Chapter 36 — Building Your Personal AI Product Stack

• Research layer
• Reasoning layer
• Creation layer
• Data layer
• Agent layer
• Evaluation layer
• Communication layer
• Selecting the minimum viable tool stack
• Avoiding tool sprawl
• Designing a replaceable AI stack

Chapter 37 — The 30-Day AI Product Manager Transformation

Week 1 — Research

Build an AI-assisted research workflow.

Week 2 — Prototype

Turn a product problem into a working prototype.

Week 3 — Agents

Build a workflow or agent that performs a real task.

Week 4 — Evaluation and Launch

Measure quality, test with users, calculate economics, and define the production path.

Final deliverable

A working AI product experiment—not a collection of prompts or tutorials.

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Appendices

Appendix A — AI Product Canvas

A complete one-page framework covering:

• Problem
• User
• Context
• Opportunity
• AI capability
• Workflow
• Data
• Model
• Tools
• Risk
• Cost
• Metrics

Appendix B — AI Product Requirements Document

A practical PRD template for AI features and AI-native products.

Appendix C — Agent Specification

• Goal
• Inputs
• Context
• Tools
• Memory
• Permissions
• Output
• Evaluation
• Failure conditions
• Escalation

Appendix D — AI Evaluation Template

• Test case
• Input
• Expected behavior
• Actual output
• Score
• Failure category
• Severity
• Regression status

Appendix E — AI Risk Register

• Risk
• Probability
• Impact
• Mitigation
• Owner
• Monitoring

Appendix F — AI Unit Economics Calculator

• Users
• Tasks
• Tokens
• Model
• Cost per task
• Infrastructure
• Revenue
• Gross margin

Appendix G — AI Product Launch Checklist

• Product
• UX
• Model
• Data
• Evaluation
• Security
• Privacy
• Cost
• Monitoring
• Support
• Rollback

Appendix H — Product Decision Log

• Decision
• Context
• Alternatives
• Evidence
• Decision
• Expected outcome
• Review date

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Book positioning

AI Product Manager is a practical guide for Product Managers, founders, designers, and technology leaders who need to build products in an environment where AI can participate in research, design, development, decision-making, and execution.

The book deliberately avoids becoming a catalog of prompts or a tutorial for a particular AI platform. Instead, it provides a durable product-development framework that remains useful as models and tools change.

The central proposition is simple:

The next generation of Product Managers will not merely manage teams that build AI products. They will design systems in which people, models, agents, data, and software work together as one product-development organization.

This structure gives the publisher a book that is positioned not as another introductory AI title, but as a practical product-management framework for the agentic AI era.